The Devil is in the Details: Simple Remedies for Image-to-LiDAR Representation Learning

2025-01-16

AI Summary

This paper identifies three fundamental design flaws long overlooked in image-LiDAR cross-modal representation learning: (1) suboptimal LiDAR coordinate system selection, (2) spatial quantization errors—particularly distortions induced by cylindrical coordinate parameterization and voxelization—and (3) underutilization of asynchronous multi-sensor data. To address these issues, we propose a lightweight correction framework comprising: a sparse-convolution-based 3D backbone network, coordinate-system remapping, adaptive voxelization, and asynchronous frame fusion—without requiring complex auxiliary loss functions. Evaluated on nuScenes 3D semantic segmentation and KITTI 3D object detection, our approach achieves +16% and +13% performance gains, respectively, significantly outperforming state-of-the-art knowledge distillation methods. Our work establishes an interpretable, low-overhead architectural optimization paradigm for multimodal perception, grounded in principled geometric and temporal modeling.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

LiDAR is a crucial sensor in autonomous driving, commonly used alongside cameras. By exploiting this camera-LiDAR setup and recent advances in image representation learning, prior studies have shown the promising potential of image-to-LiDAR distillation. These prior arts focus on the designs of their own losses to effectively distill the pre-trained 2D image representations into a 3D model. However, the other parts of the designs have been surprisingly unexplored. We find that fundamental design elements, e.g., the LiDAR coordinate system, quantization according to the existing input interface, and data utilization, are more critical than developing loss functions, which have been overlooked in prior works. In this work, we show that simple fixes to these designs notably outperform existing methods by 16% in 3D semantic segmentation on the nuScenes dataset and 13% in 3D object detection on the KITTI dataset in downstream task performance. We focus on overlooked design choices along the spatial and temporal axes. Spatially, prior work has used cylindrical coordinate and voxel sizes without considering their side effects yielded with a commonly deployed sparse convolution layer input interface, leading to spatial quantization errors in 3D models. Temporally, existing work has avoided cumbersome data curation by discarding unsynced data, limiting the use to only the small portion of data that is temporally synced across sensors. We analyze these effects and propose simple solutions for each overlooked aspect.

Problem

• Image-LiDAR Fusion
• Autonomous Vehicles
• Spatial-Temporal Synchronization

Innovation

• LiDAR-Camera Fusion
• 3D Environment Understanding
• Spatial and Temporal Information Processing